Dr. Vidhu Shekhar

Security has various dimensions in fintech. Data privacy and regulatory requirements may necessitate additional security measures, such as deploying servers on the client's premises. However, the base-level security provided by cloud computing is always beneficial. Cloud providers invest heavily in robust security measures like encryption, multi-factor authentication, and network segmentation. They also adhere to industry regulations and standards, such as PCI DSS for handling credit card data and GDPR for safeguarding personal information.

The elasticity edge of cloud computing can be very helpful in financial technology. With distinction in usage load based on market hours, bull or bear state of the market, weekend, etc., that are natural in finance, flexible load handling ability to quickly scale resources up or down and sudden spikes in traffic can be crucial. This elasticity allows for seamless accommodation of growth without the need for significant upfront investments in infrastructure. By leveraging auto-scaling capabilities, financial applications can automatically adjust their resources to maintain optimal performance during peak periods while minimizing costs during low-demand times.

Regulatory knowledge is critical for FinTech professionals. Regulations can be called a basic characteristic of the finance industry. Automating or digitizing anything in finance often involves deconstructing the financial process dominated by regulatory requirements. Errors in it can lead to severe consequences, including firms being banned from conducting business. FinTech professionals must understand and apply relevant laws and regulations to ensure compliance, manage risks, develop compliant products, and collaborate with legal & compliance teams. Evaluation should involve their understanding of regulations, ability to apply this knowledge in their work, proactiveness in staying updated, and collaboration skills.

Optimization via AI tools will have automatic classification, categorization and data tagging based on content and metadata. This enables more efficient storage utilization, reducing redundancy and optimizing storage space allocation. AI can also dynamically adapt storage policies and tier data based on usage patterns and access frequency, ensuring that frequently accessed data is stored on high-performace storage while less critical data is moved to cost effective storage tiers. Such intelligent optimization enhances overall storage performance and cost efficiency.

Adaptability and Critical Thinking are almost two sides of the same coin. Both are important characteristics of a good programmer. Therefore, demonstrating adaptability during an AI job interview is crucial. Discuss instances where you successfully navigated an unfamiliar challenge or rapidly acquired new skills to solve problems. Discuss your approach to staying updated with the latest AI advancements and how you incorporate them into your work. Show enthusiasm for continuous learning and embracing new technologies or methodologies. Share your experience of how you adapted to changing project requirements, team dynamics, or business needs. Emphasize your flexibility in working across different domains, tools, or programming languages.

Since the setup is about facing an imbalanced dataset in an AI interview, you can showcase your expertise by demonstrating knowledge of advanced techniques. Ensemble methods, like bagging or boosting, can be used to improve performance on minority class. Another strategy could be anomaly detection, where you train a model on majority class and identify instances from minority class as anomalies. Cost-sensitive learning can allow you to assign higher misclassification costs to minority class. In scenarios with extreme imbalance, one-class SVM or Isolation forests, can be employed. One can perhaps also think about discussing potential of using generative models like GANs or VAEs, to synthesize new examples of minority class,